DESCRIPTION (provided by investigator): This is a competing renewal application to continue support for the Family Relationships in Late Life project (FRILL; R01 AG15321). Long-term objectives are to: (1) specify a predictive profile of the quality of care (QC) informal caregivers are likely to provide to community-residing frail and disabled elders, (2) develop a brief portable instrument useful to practitioners that characterizes QC and demonstrate that it can be used widely, and (3) demonstrate the potential utility of this profile and instrument in early identification and appropriate treatment to improve not only quality of informal care but also caregiver well-being (both while providing care and after caregiving duties end). The proposed follow-up study (FRILL2) builds on existing infrastructure and accomplishments and extends FRILL in several new directions. FRILL2 will enroll 500 coresiding caregiver-care recipient dyads for 3 longitudinal assessments at 18-month intervals, and an entirely new component will follow caregivers who transition out of caregiving (e.g., through care recipient death or institutionalization) at 6-month intervals. QC assessment will be expanded to include not only indicators of maltreatment but also care that ranges through sufficient to exemplary. Using refined models, methods, and measures, FRILL2 will: (1) determine the extent to which predisposing factors (e.g., amount of care provided) and caregiver mental health (CGMH; depression, anger, anxiety, and cognitive impairment) predict current and future indicators of the full range of QC, caregiving transitions, and long-term caregiver well-being, (2) over-sample African American dyads at intake to produce a sample adequate for longitudinal comparisons between Whites and African Americans in the pre- and post-transition caregiving experience, and (3) test hypothesized cross-sectional and longitudinal associations between predisposing factors, CGMH, QC, caregiving transitions, and post-transition caregiver well-being. Tests of hypotheses and model fit will employ structural equations modeling, latent growth modeling, and latent transition analytic techniques.